System and method for providing patient status feedback via an automated patient care system with speech-based wellness monitoring

ABSTRACT

A system and method for providing patient status feedback via an automated patient care system with speech-based wellness monitoring are described. Device measures are collected through an implantable medical device on a substantially continuous basis from an implant recipient. The device measures are received as physiological measures for storage into a patient care record. The physiological measures include at least one of collected or derived physiological measures. Patient wellness indicators are obtained through voice feedback provided by the implant recipient substantially contemporaneous to the collection of at least one set of the device measures. The voice feedback is processed against a stored speech vocabulary into normalized quality of life measures for storage into the patient care record. The physiological measures and the quality of life measures stored in the patient care record are analyzed relative to at least one of other physiological measures and other quality of life measures to generate patient status feedback.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of Ser. No. 09/361,777, Jul.26, 1999 U.S. Pat. No. 6,203,495, issued Mar. 20, 2001, which is acontinuation-in-part of U.S. patent application, Ser. No. 09/324,894,filed Jun. 3, 1999, pending, the priority filing dates of which areclaimed and the disclosures of which are incorporated by reference.

FIELD OF THE INVENTION

The present invention relates in general to automated data collectionand analysis, and, in particular, to a system and method for providingpatient status feedback via all automated patient care system withspeech-based wellness monitoring.

BACKGROUND OF THE INVENTION

Implantable pulse generators (IPGs) are medical devices commonly used totreat irregular heartbeats, known as arrhythmias. There are three basictypes. Cardiac pacemakers are used to manage bradycardia, an abnormallyslow or irregular heartbeat. Bradycardia can cause symptoms such asfatigue, dizziness, and fainting. Implantable cardioverterdefibrillators (ICDs) are used to treat tachycardia, heart rhythms thatare abnormally fast and life threatening. Tachycardia can result insudden cardiac death (SCD). Implantable cardiovascular monitors andtherapeutic devices are used to monitor and treat structural problems ofthe heart, such as congestive heart failure, as well as rhythm problems.

Pacemakers and ICDs are equipped with an on-board, volatile memory inwhich telemetered signals can be stored for later retrieval andanalysis. In addition, a growing class of cardiac medical devices,including implantable heart failure monitors, implantable eventmonitors, cardiovascular monitors, and therapy devices, are being usedto provide similar stored device information. These devices are able tostore more than thirty minutes of per heartbeat data. Typically, thetelemetered signals can provide patient device information recorded on aper heartbeat, binned average basis, or derived basis from, for example,atrial electrical activity, ventricular electrical activity, minuteventilation, patient activity score, cardiac output score, mixed venousoxygen score, cardiovascular pressure measures, time of day, and anyinterventions and the relative success of such interventions.Telemetered signals are also stored in a broader class of monitors andtherapeutic devices for other areas of medicine, including metabolism,endocrinology, hematology, neurology, muscular disorders,gastroenterology, urology, ophthalmology, otolaryngology, orthopedics,and similar medical subspecialties.

Presently, stored device information is retrieved using a proprietaryinterrogator or programmer, often during a clinic visit or following adevice event. The volume of data retrieved from a single deviceinterrogation “snapshot” can be large and proper interpretation andanalysis can require significant physician time and detailedsubspecialty knowledge, particularly by cardiologists and cardiacelectrophysiologists. The sequential logging and analysis of regularlyscheduled interrogations can create an opportunity for recognizingsubtle and incremental changes in patient condition otherwiseundetectable by inspection of a single “snapshot.” However, presentapproaches to data interpretation and understanding and practicallimitations on time and physician availability make such analysisimpracticable.

Similarly, the determination and analysis of the quality of life issueswhich typically accompany the onset of a chronic yet stable diseases,such as coronary-artery disease, is a crucial adjunct to assessingpatient wellness and progress. However, unlike in a traditional clinicalsetting, physicians participating in providing remote patient care arenot able to interact with their patients in person. Consequently,quality of life measures, such as how the patient subjectively looks andfeels, whether the patient has shortness of breath, can work, can sleep,is depressed, is sexually active, can perform activities of daily life,and so on, cannot be implicitly gathered and evaluated.

A prior art system for collecting and analyzing pacemaker and ICDtelemetered signals in a clinical or office setting is the Model 9790Programmer, manufactured by Medtronic, Inc., Minneapolis, Minn. Thisprogrammer can be used to retrieve data, such as patientelectrocardiogram and any measured physiological conditions, collectedby the IPG for recordation, display and printing. The retrieved data isdisplayed in chronological order and analyzed by a physician. Comparableprior art systems are available from other IPG manufacturers, such asthe Model 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind., which includes a removable floppydiskette mechanism for patient data storage. These prior art systemslack remote communications facilities and must be operated with thepatient present. These systems present a limited analysis of thecollected data based on a single device interrogation and lack thecapability to recognize trends in the data spanning multiple episodesover time or relative to a disease specific peer group.

A prior art system for locating and communicating with a remote medicaldevice implanted in an ambulatory patient is disclosed in U.S. Pat. No.5,752,976 ('976). The implanted device includes a telemetry transceiverfor communicating data and operating instructions between the implanteddevice and an external patent communications device. The communicationsdevice includes a communication link to a remote medical supportnetwork, a global positioning satellite receiver, and a patientactivated link for permitting patient initiated communication with themedical support network. Patient voice communications through thepatient link include both actual patient voice and manually actuatedsignaling which may convey an emergency situation. The patient voice isconverted to an audio signal, digitized, encoded, and transmitted bydata bus to a system controller.

Related prior art systems for remotely communicating with and receivingtelemetered signals from a medical device are disclosed in U.S. Pat.Nos. 5,113,869 ('869) and 5,336,245 ('245). In the '869 patent, animplanted AECG monitor can be automatically interrogated at preset timesof day to telemeter out accumulated data to a telephonic communicator ora full disclosure recorder. The communicator can be automaticallytriggered to establish a telephonic communication link and transmit theaccumulated data to an office or clinic through a modem. in the '245patent, telemetered data is downloaded to a larger capacity, externaldata recorder and is forwarded to a clinic using an auto-dialer and faxmodem operating in a personal computer-based programmer/interrogator.However, the '976 telemetry transceiver, '869 communicator, and '245programmer/interrogator are limited to facilitating communication andtransferal of downloaded patient data and do not include an ability toautomatically track, recognize, and analyze trends in the data itself.Moreover, the '976 telemetry transceiver facilitates patient voicecommunications through transmission of a digitized audio signal and doesnot perform voice recognition or other processing to the patient'svoice.

Thus, there is a need for a system and method for providing continuousretrieval, transferal, and automated analysis of retrieved implantablemedical device information, such as telemetered signals, retrieved ingeneral from a broad class of implantable medical devices and, inparticular, from IPGs and cardiovascular monitors. Preferably, theautomated analysis would include recognizing a trend and determiningwhether medical intervention is necessary.

There is a further need for a system and method that would allowconsideration of sets of collected measures, both actual and derived,from multiple device interrogations. These collected measures sets couldthen be compared and analyzed against short and long term periods ofobservation.

There is a further need for a system and method that would enable themeasures sets for an individual patient to be self-referenced andcross-referenced to similar or dissimilar patients and to the generalpatient population. Preferably, the historical collected measures setsof an individual patient could be compared and analyzed against those ofother patients in general or of a disease specific peer group inparticular.

There is a further need for a system and method for accepting andnormalizing live voice feedback spoken by an individual patient while anidentifiable set of telemetered signals is collected by a implantablemedical device. Preferably, the normalized voice feedback asemi-quantitative self-assessment of an individual patient's physicaland emotional well being at a time substantially contemporaneous to thecollection of the telemetered signals.

SUMMARY OF THE INVENTION

The present invention provides a system and method for automatedcollection and analysis of patient information retrieved from animplantable medical device for remote patient care. The patient deviceinformation relates to individual measures recorded by and retrievedfrom implantable medical devices, such as IPGs and monitors. The patientdevice information is received on a regular, e.g., daily, basis as setsof collected measures which are stored along with other patient recordsin a database. The information can be analyzed in an automated fashionand feedback provided to the patient at any time and in any location.

The present invention also provides a system and method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system. As before, patient deviceinformation is received on a regular, e.g., daily, basis as sets ofcollected measures which are stored along with other patient records ina database. Voice feedback spoken by an individual patient is processedinto a set of quality of life measures by a remote client substantiallycontemporaneous to the recordation of an identifiable set of collecteddevice measures by the implantable medical device. The processed voicefeedback and identifiable collected device measures set are bothreceived and stored into the patient record in the database forsubsequent evaluation.

An embodiment of the present invention is a system and method foranalyzing normalized patient voice feedback in an automated collectionand analysis patient care system. Device measures providingphysiological measures collected by an implantable medical device on asubstantially continuous basis are received for storage into a patientcare record. Voice feedback spoken by an individual patientsubstantially contemporaneous to the collection of at least one set ofthe device measures is received. The voice feedback is processed intonormalized quality of life measures for storage into the patient carerecord. The physiological measures and the quality of life measuresstored in the patient care record are analyzed relative to at least oneof other physiological measures and other quality of life measures todetermine a patient status indicator.

A further embodiment is a system and method for providing patient statusfeedback via an automated patient care system with speech-based wellnessmonitoring. Device measures are collected through an implantable medicaldevice on a substantially continuous basis from an implant recipient.The device measures are received as physiological measures for storageinto a patient care record. The physiological measures include at leastone of collected or derived physiological measures. Patient wellnessindicators are obtained through voice feedback provided by the implantrecipient substantially contemporaneous to the collection of at leastone set of the device measures. The voice feedback is processed againsta stored speech vocabulary into normalized quality of life measures forstorage into the patient care record. The physiological measures and thequality of life measures stored in the patient care record are analyzedrelative to at least one of other physiological measures and otherquality of life measures to generate patient status feedback.

A further embodiment is a system and method for interactively monitoringpatient status in an automated patient care system using voice feedback.Physiological measures are monitored for an implant recipient. Devicemeasures are collected through an implantable medical device on asubstantially continuous basis from the implant recipient. The devicemeasures are periodically stored as at least one of collected or derivedphysiological measures into an individual patient care record. Qualityof life measures are monitored for the implant recipient. Patientwellness indicators are obtained through voice feedback provided by theimplant recipient substantially contemporaneous to the collection of thedevice measures. The voice feedback is processed against a stored speechgrammar and vocabulary. The processed voice feedback is stored asstandardized quality of life measures into the patient care record. Thephysiological measures and the quality of life measures from the patientcare record are recurrently evaluated against at least one of otherphysiological measures and other quality of life measures to generate apatient status indicator.

The present invention facilitates the gathering, storage, and analysisof critical patient information obtained on a routine basis and analyzedin an automated manner. Thus, the burden on physicians and trainedpersonnel to evaluate the volumes of information is significantlyminimized while the benefits to patients are greatly enhanced.

The present invention also enables the simultaneous collection of bothphysiological measures from implantable medical devices and quality oflife measures spoken in the patient's own words. Voice recognitiontechnology enables the spoken patient feedback to be normalized to astandardized set of semi-quantitative quality of life measures, therebyfacilitating holistic remote, automated patient care.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for automated collection andanalysis of patient information retrieved from an implantable medicaldevice for remote patient care in accordance with the present invention;

FIG. 2 is a block diagram showing the hardware components of the serversystem of the system of FIG. 1;

FIG. 3 is a block diagram showing the software modules of the serversystem of the system of FIG. 1;

FIG. 4 is a block diagram showing the analysis module of the serversystem of FIG. 3

FIG. 5 is a database schema showing, by way of example, the organizationof a cardiac patient care record stored in the database of the system ofFIG. 1;

FIG. 6 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database of the system ofFIG. 1;

FIG. 7 is a flow diagram showing a method for automated collection andanalysis of patient information retrieved from an implantable medicaldevice for remote patient care in accordance with the present invention;

FIG. 8 is a flow diagram showing a routine for analyzing collectedmeasures sets for use in the method of FIG. 7;

FIG. 9 is a flow diagram showing a routine for comparing siblingcollected measures sets for use in the routine of FIG. 8;

FIGS. 10A and 10B are flow diagrams showing a routine for comparing peercollected measures sets for use in the routine of FIG. 8;

FIG. 11 is a flow diagram showing a routine for providing feedback foruse in the method of FIG. 7;

FIG. 12 is a block diagram showing a system for providing patient statusfeedback via an automated patient care system with speech-based wellnessmonitoring;

FIG. 13 is a block diagram showing the software modules of the remoteclient of the system of FIG. 12;

FIG. 14 is a block diagram showing the software modules of the serversystem of the system of FIG. 12;

FIG. 15 is a database schema showing, by way of example, theorganization of a quality of life record for cardiac patient care storedas part of a patient care record in the database of the system of FIG.12;

FIGS. 16A-16B are flow diagrams showing a method for providing patientstatus feedback via an automated patient care system with speech-basedwellness monitoring;

FIG. 17 is a flow diagram showing a routine for processing voicefeedback for use in the method of FIGS. 16A-16B;

FIG. 18 is a flow diagram showing a routine for requesting a quality oflife measure for use in the routine of FIG. 17;

FIG. 19 is a flow diagram showing a routine for recognizing andtranslating individual spoken words for use in the routine of FIG. 17;and

FIG. 20 is a block diagram showing the software modules of the serversystem in a further embodiment of the system of FIG. 12.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing a system 10 for automated collectionand analysis of patient information retrieved from an implantablemedical device for remote patient care in accordance with the presentinvention. A patient 11 is a recipient of an implantable medical device12, such as, by way of example, an IPG or a heart failure or eventmonitor, with a set of leads extending into his or her heart. Theimplantable medical device 12 includes circuitry for recording into ashort-term, volatile memory telemetered signals, which are stored as aset of collected measures for later retrieval.

For an exemplary cardiac implantable medical device, the telemeteredsignals non-exclusively present patient information recorded on a perheartbeat, binned average or derived basis and relating to: atrialelectrical activity, ventricular electrical activity, minuteventilation, patient activity score, cardiac output score, mixed venousoxygenation score, cardiovascular pressure measures, time of day, thenumber and types of interventions made, and the relative success of anyinterventions, plus the status of the batteries and programmed settings.Examples of pacemakers suitable for use in the present invention includethe Discovery line of pacemakers, manufactured by Guidant Corporation,Indianapolis, Ind. Examples of ICDs suitable for use in the presentinvention include the Gem line of ICDs, manufactured by MedtronicCorporation, Minneapolis, Minn.

In the described embodiment, the patient 11 has a cardiac implantablemedical device. However, a wide range of related implantable medicaldevices are used in other areas of medicine and a growing number ofthese devices are also capable of measuring and recording patientinformation for later retrieval. These implantable medical devicesinclude monitoring and therapeutic devices for use in metabolism,endocrinology, hematology, neurology, muscular disorders,gastroenterology, urology, ophthalmology, otolaryngology, orthopedics,and similar medical subspecialties. One skilled in the art would readilyrecognize the applicability of the present invention to these relatedimplantable medical devices.

On a regular basis, the telemetered signals stored in the implantablemedical device 12 are retrieved. By way of example, a programmer 14 canbe used to retrieve the telemetered signals. However, any form ofprogrammer, interrogator, recorder, monitor, or telemetered signalstransceiver suitable for communicating with an implantable medicaldevice 12 could be used, as is known in the art. In addition, a personalcomputer or digital data processor could be interfaced to theimplantable medical device 12, either directly or via a telemeteredsignals transceiver configured to communicate with the implantablemedical device 12.

Using the programmer 14, a magnetized reed switch (not shown) within theimplantable medical device 12 closes in response to the placement of awand 13 over the location of the implantable medical device 12. Theprogrammer 14 communicates with the implantable medical device 12 via RFsignals exchanged through the wand 13. Programming or interrogatinginstructions are sent to the implantable medical device 12 and thestored telemetered signals are downloaded into the programmer 14. Oncedownloaded, the telemetered signals are sent via an internetwork 15,such as the Internet, to a server system 16 which periodically receivesand stores the telemetered signals in a database 17, as furtherdescribed below with reference to FIG. 2.

An example of a programmer 14 suitable for use in the present inventionis the Model 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind., which includes the capability to storeretrieved telemetered signals on a proprietary removable floppydiskette. The telemetered signals could later be electronicallytransferred using a personal computer or similar processing device tothe internetwork 15, as is known in the art.

Other alternate telemetered signals transfer means could also beemployed. For instance, the stored telemetered signals could beretrieved from the implantable medical device 12 and electronicallytransferred to the internetwork 15 using the combination of a remoteexternal programmer and analyzer and a remote telephonic communicator,such as described in U.S. Pat. No. 5,113,869, the disclosure of which isincorporated herein by reference. Similarly, the stored telemeteredsignals could be retrieved and remotely downloaded to the server system16 using a world-wide patient location and data telemetry system, suchas described in U.S. Pat. No. 5,752,976, the disclosure of which isincorporated herein by reference.

The received telemetered signals are analyzed by the server system 16,which generates a patient status indicator. The feedback is thenprovided back to the patient 11 through a variety of means. By way ofexample, the feedback can be sent as an electronic mail messagegenerated automatically by the server system 16 for transmission overthe internetwork 15. The electronic mail message is received by a remoteclient 18, such as a personal computer (PC), situated for local accessby the patient 11. Alternatively, the feedback can be sent through atelephone interface device 19 as an automated voice mail message to atelephone 21 or as an automated facsimile message to a facsimile machine22, both also situated for local access by the patient 11. In additionto a remote client 18, telephone 21, and facsimile machine 22, feedbackcould be sent to other related devices, including a network computer,wireless computer, personal data assistant, television, or digital dataprocessor. Preferably, the feedback is provided in a tiered fashion, asfurther described below with reference to FIG. 3.

FIG. 2 is a block diagram showing the hardware components of the serversystem 16 of the system 10 of FIG. 1. The server system 16 consists ofthree individual servers: network server 31, database server 34, andapplication server 35. These servers are interconnected via anintranetwork 33. In the described embodiment, the functionality of theserver system 16 is distributed among these three servers for efficiencyand processing speed, although the functionality could also be performedby a single server or cluster of servers. The network server 31 is theprimary interface of the server system 16 onto the internetwork 15. Thenetwork server 31 periodically receives the collected telemeteredsignals sent by remote implantable medical devices over the internetwork15. The network server 31 is interfaced to the internetwork 15 through arouter 32. To ensure reliable data exchange, the network server 31implements a TCP/IP protocol stack, although other forms of networkprotocol stacks are suitable.

The database server 34 organizes the patient care records in thedatabase 17 and provides storage of and access to information held inthose records. A high volume of data in the form of collected measuressets from individual patients is received. The database server 34 freesthe network server 31 from having to categorize and store the individualcollected measures sets in the appropriate patient care record.

The application server 35 operates management applications and performsdata analysis of the patient care records, as further described belowwith reference to FIG. 3. The application server 35 communicatesfeedback to the individual patients either through electronic mail sentback over the internetwork 15 via the network server 31 or as automatedvoice mail or facsimile messages through the telephone interface device19.

The server system 16 also includes a plurality of individualworkstations 36 (WS) interconnected to the intranetwork 33, some ofwhich can include peripheral devices, such as a printer 37. Theworkstations 36 are for use by the data management and programmingstaff, nursing staff, office staff, and other consultants and authorizedpersonnel.

The database 17 consists of a high-capacity storage medium configured tostore individual patient care records and related health careinformation. Preferably, the database 17 is configured as a set ofhigh-speed, high capacity hard drives, such as organized into aRedundant Array of Inexpensive Disks (RAID) volume. However, any form ofvolatile storage, non-volatile storage, removable storage, fixedstorage, random access storage, sequential access storage, permanentstorage, erasable storage, and the like would be equally suitable. Theorganization of the database 17 is further described below withreference to FIG. 3.

The individual servers and workstations are general purpose, programmeddigital computing devices consisting of a central processing unit (CPU),random access memory (RAM), non-volatile secondary storage, such as ahard drive or CD ROM drive, network interfaces, and peripheral devices,including user interfacing means, such as a keyboard and display.Program code, including software programs, and data are loaded into theRAM for execution and processing by the CPU and results are generatedfor display, output, transmittal, or storage. In the describedembodiment, the individual servers are Intel Pentium-based serversystems, such as available from Dell Computers, Austin, Tex., or CompaqComputers, Houston, Tex. Each system is preferably equipped with 128MBRAM, 100GB hard drive capacity, data backup facilities, and relatedhardware for interconnection to the intranetwork 33 and internetwork 15.In addition, the workstations 36 are also Intel Pentium-based personalcomputer or workstation systems, also available from Dell Computers,Austin, Tex., or Compaq Computers, Houston, Tex. Each workstation ispreferably equipped with 64MB RAM, 10GB hard drive capacity, and relatedhardware for interconnection to the intranetwork 33. Other types ofserver and workstation systems, including personal computers,minicomputers, mainframe computers, supercomputers, parallel computers,workstations, digital data processors and the like would be equallysuitable, as is known in the art.

The telemetered signals are communicated over an internetwork 15, suchas the Internet. However, any type of electronic communications linkcould be used, including an intranetwork link, serial link, datatelephone link, satellite link, radio-frequency link, infrared link,fiber optic link, coaxial cable link, television link, and the like, asis known in the art. Also, the network server 31 is interfaced to theinternetwork 15 using a T−1 network router 32, such as manufactured byCisco Systems, Inc., San Jose, Calif. However, any type of interfacingdevice suitable for interconnecting a server to a network could be used,including a data modem, cable modem, network interface, serialconnection, data port, hub, frame relay, digital PBX, and the like, asis known in the art.

FIG. 3 is a block diagram showing the software modules of the serversystem 16 of the system 10 of FIG. 1. Each module is a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU as object or byte code, as is known in the arts. The variousimplementations of the source code and object and byte codes can be heldon a computer-readable storage medium or embodied on a transmissionmedium in a carrier wave. There are three basic software modules, whichfunctionally define the primary operations performed by the serversystem 16: database module 51, analysis module 53, and feedback module55. In the described embodiment, these modules are executed in adistributed computing environment, although a single server or a clusterof servers could also perform the functionality of the modules. Themodule functions are further described below in more detail beginningwith reference to FIG. 7.

For each patient being provided remote patient care, the server system16 periodically receives a collected measures set 50 which is forwardedto the database module 51 for processing. The database module 51organizes the individual patent care records stored in the database 52and provides the facilities for efficiently storing and accessing thecollected measures sets 50 and patient data maintained in those records.An exemplary database schema for use in storing collected measures sets50 in a patient care record is described below, by way of example, withreference to FIG. 5. The database server 34 (shown in FIG. 2) performsthe functionality of the database module 51. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

The analysis module 53 analyzes the collected measures sets 50 stored inthe patient care records in the database 52. The analysis module 53makes an automated determination of patient wellness in the form of apatient status indicator 54. Collected measures sets 50 are periodicallyreceived from implantable medical devices and maintained by the databasemodule 51 in the database 52. Through the use of this collectedinformation, the analysis module 53 can continuously follow the medicalwell being of a patient and can recognize any trends in the collectedinformation that might warrant medical intervention. The analysis module53 compares individual measures and derived measures obtained from boththe care records for the individual patient and the care records for adisease specific group of patients or the patient population in general.The analytic operations performed by the analysis module 53 are furtherdescribed below with reference to FIG. 4. The application server 35(shown in FIG. 2) performs the functionality of the analysis module 53.

The feedback module 55 provides automated feedback to the individualpatient based, in part, on the patient status indicator 54. As describedabove, the feedback could be by electronic mail or by automated voicemail or facsimile. Preferably, the feedback is provided in a tieredmanner. In the described embodiment, four levels of automated feedbackare provided. At a first level, an interpretation of the patient statusindicator 54 is provided. At a second level, a notification of potentialmedical concern based on the patient status indicator 54 is provided.This feedback level could also be coupled with human contact byspecially trained technicians or medical personnel. At a third level,the notification of potential medical concern is forwarded to medicalpractitioners located in the patient's geographic area. Finally, at afourth level, a set of reprogramming instructions based on the patientstatus indicator 54 could be transmitted directly to the implantablemedical device to modify the programming instructions contained therein.As is customary in the medical arts, the basic tiered feedback schemewould be modified in the event of bona fide medical emergency. Theapplication server 35 (shown in FIG. 2) performs the functionality ofthe feedback module 55.

FIG. 4 is a block diagram showing the analysis module 53 of the serversystem 16 of FIG. 3. The analysis module 53 contains two functionalsubmodules: comparison module 62 and derivation module 63. The purposeof the comparison module 62 is to compare two or more individualmeasures, either collected or derived. The purpose of the derivationmodule 63 is to determine a derived measure based on one or morecollected measures which is then used by the comparison module 62. Forinstance, a new and improved indicator of impending heart failure couldbe derived based on the exemplary cardiac collected measures setdescribed with reference to FIG. 5. The analysis module 53 can operateeither in a batch mode of operation wherein patient status indicatorsare generated for a set of individual patients or in a dynamic modewherein a patient status indicator is generated on the fly for anindividual patient.

The comparison module 62 receives as inputs from the database 17 twoinput sets functionally defined as peer collected measures sets 60 andsibling collected measures sets 61, although in practice, the collectedmeasures sets are stored on a per sampling basis. Peer collectedmeasures sets 60 contain individual collected measures sets that allrelate to the same type of patient information, for instance, atrialelectrical activity, but which have been periodically collected overtime. Sibling collected measures sets 61 contain individual collectedmeasures sets that relate to different types of patient information, butwhich may have been collected at the same time or different times. Inpractice, the collected measures sets are not separately stored as“peer” and “sibling” measures. Rather, each individual patient carerecord stores multiple sets of sibling collected measures. Thedistinction between peer collected measures sets 60 and siblingcollected measures sets 61 is further described below with reference toFIG. 6.

The derivation module 63 determines derived measures sets 64 on anas-needed basis in response to requests from the comparison module 62.The derived measures 64 are determined by performing linear andnon-linear mathematical operations on selected peer measures 60 andsibling measures 61, as is known in the art.

FIG. 5 is a database schema showing, by way of example, the organizationof a cardiac patient care record stored 70 in the database 17 of thesystem 10 of FIG. 1. Only the information pertaining to collectedmeasures sets are shown. Each patient care record would also containnormal identifying and treatment profile information, as well as medicalhistory and other pertinent data (not shown). Each patient care recordstores a multitude of collected measures sets for an individual patient.Each individual set represents a recorded snapshot of telemeteredsignals data which was recorded, for instance, per heartbeat or binnedaverage basis by the implantable medical device 12. For example, for acardiac patient, the following information would be recorded as acollected measures set: atrial electrical activity 71, ventricularelectrical activity 72, time of day 73, activity level 74, cardiacoutput 75, oxygen level 76, cardiovascular pressure measures 77,pulmonary measures 78, interventions made by the implantable medicaldevice 78, and the relative success of any interventions made 80. Inaddition, the implantable medical device 12 would also communicatedevice specific information, including battery status 81 and programsettings 82. Other types of collected measures are possible. Inaddition, a well-documented set of derived measures can be determinedbased on the collected measures, as is known in the art.

FIG. 6 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database 17 of the system 10of FIG. 1. Three patient care records are shown for Patient 1, Patient2, and Patient 3. For each patent, three sets of measures are shown, X,Y, and Z. The measures are organized into sets with Set 0 representingsibling measures made at a reference time t=0. Similarly, Set n−2, Setn−1 and Set n each represent sibling measures made at later referencetimes t=n−2, t=n−1 and t=n, respectively.

For a given patient, for instance, Patient 1, all measures representingthe same type of patient information, such as measure X, are peermeasures. These are measures, which are monitored over time in adisease-matched peer group. All measures representing different types ofpatient information, such as measures X, Y, and Z, are sibling measures.These are measures which are also measured over time, but which mighthave medically significant meaning when compared to each other within asingle set. Each of the measures, X, Y, and Z, could be either collectedor derived measures.

The analysis module 53 (shown in FIG. 4) performs two basic forms ofcomparison. First, individual measures for a given patient can becompared to other individual measures for that same patient. Thesecomparisons might be peer-to-peer measures projected over time, forinstance, X_(n), X_(n−1), X_(n−2), . . . X₀, or sibling-to-siblingmeasures for a single snapshot, for instance, X_(n), Y_(n), and Z_(n),or projected over time, for instance, X_(n), Y_(n), Z_(n), X_(n−1),Y_(n−1), Z_(n−1), X_(n−2), Y_(n−2), Z_(n−2), . . . X₀, Y₀, Z₀. Second,individual measures for a given patient can be compared to otherindividual measures for a group of other patients sharing the samedisease-specific characteristics or to the patient population ingeneral. Again, these comparisons might be peer-to-peer measuresprojected over time, for instance, X_(n), X_(n′), X_(n″), X_(n−1),X_(n−1′), X_(n−1″), X_(n−2), X_(n−2′), X_(n−2″). . . X₀, X_(0′), X_(0″),or comparing the individual patient's measures to an average from thegroup. Similarly, these comparisons might be sibling-to-sibling measuresfor single snapshots, for instance, X_(n), X_(n′), X_(n″), Y_(n),Y_(n′), Y_(n″), and Z_(n), Z_(n′), Z_(n″), or projected over time, forinstance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), Z_(n), Z_(n′),Z_(n″), X_(n−1), X_(n−1′), X_(n−1″), Y_(n−1),Y_(n−1′), Y_(n−1″),Z_(n−1), Z_(n−′), Z_(n−1″), X_(n−2), X_(n−2′), X_(n−2″), Y_(n−2),Y_(n−2′), Y_(n−2″), Z_(n−2), Z_(n−2′), Z_(n−2″). . . X₀, X_(0′), X_(0″),Y₀, Y_(0′), Y_(0″), and Z₀, Z_(0′), Z_(0″). Other forms of comparisonsare feasible.

FIG. 7 is a flow diagram showing a method 90 for automated collectionand analysis of patient information retrieved from an implantablemedical device 12 for remote patient care in accordance with the presentinvention. The method 90 is implemented as a conventional computerprogram for execution by the server system 16 (shown in FIG. 1). As apreparatory step, the patient care records are organized in the database17 with a unique patient care record assigned to each individual patient(block 91). Next, the collected measures sets for an individual patientare retrieved from the implantable medical device 12 (block 92) using aprogrammer, interrogator, telemetered signals transceiver, and the like.The retrieved collected measures sets are sent, on a substantiallyregular basis, over the internetwork 15 or similar communications link(block 93) and periodically received by the server system 16 (block 94).The collected measures sets are stored into the patient care record inthe database 17 for that individual patient (block 95). One or more ofthe collected measures sets for that patient are analyzed (block 96), asfurther described below with reference to FIG. 8. Finally, feedbackbased on the analysis is sent to that patient over the internetwork 15as an email message, via telephone line as an automated voice mail orfacsimile message, or by similar feedback communications link (block97), as further described below with reference to FIG. 11.

FIG. 8 is a flow diagram showing the routine for analyzing collectedmeasures sets 96 for use in the method of FIG. 7. The purpose of thisroutine is to make a determination of general patient wellness based oncomparisons and heuristic trends analyses of the measures, bothcollected and derived, in the patient care records in the database 17. Afirst collected measures set is selected from a patient care record inthe database 17 (block 100). If the measures comparison is to be made toother measures originating from the patient care record for the sameindividual patient (block 101), a second collected measures set isselected from that patient care record (block 102). Otherwise, a groupmeasures comparison is being made (block 101) and a second collectedmeasures set is selected from another patient care record in thedatabase 17 (block 103). Note the second collected measures set couldalso contain averaged measures for a group of disease specific patientsor for the patient population in general.

Next, if a sibling measures comparison is to be made (block 104), aroutine for comparing sibling collected measures sets is performed(block 105), as further described below with reference to FIG. 9.Similarly, if a peer measures comparison is to be made (block 106), aroutine for comparing sibling collected measures sets is performed(block 107), as further described below with reference to FIGS. 10A and10B.

Finally, a patient status indicator is generated (block 108). By way ofexample, cardiac output could ordinarily be approximately 5.0 liters perminute with a standard deviation of ±1.0. An actionable medicalphenomenon could occur when the cardiac output of a patient is ±3.0-4.0standard deviations out of the norm. A comparison of the cardiac outputmeasures 75 (shown in FIG. 5) for an individual patient against previouscardiac output measures 75 would establish the presence of any type ofdownward health trend as to the particular patient. A comparison of thecardiac output measures 75 of the particular patient to the cardiacoutput measures 75 of a group of patients would establish whether thepatient is trending out of the norm. From this type of analysis, theanalysis module 53 generates a patient status indicator 54 and othermetrics of patient wellness, as is known in the art.

FIG. 9 is a flow diagram showing the routine for comparing siblingcollected measures sets 105 for use in the routine of FIG. 8. Siblingmeasures originate from the patient care records for an individualpatient. The purpose of this routine is either to compare siblingderived measures to sibling derived measures (blocks 111-113) or siblingcollected measures to sibling collected measures (blocks 115-117). Thus,if derived measures are being compared (block 110), measures areselected from each collected measures set (block 111). First and secondderived measures are derived from the selected measures (block 112)using the derivation module 63 (shown in FIG. 4). The first and secondderived measures are then compared (block 113) using the comparisonmodule 62 (also shown in FIG. 4). The steps of selecting, determining,and comparing (blocks 111-113) are repeated until no fisher comparisonsare required (block 114), whereupon the routine returns.

If collected measures are being compared (block 110), measures areselected from each collected measures set (block 115). The first andsecond collected measures are then compared (block 116) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting andcomparing (blocks 115-116) are repeated until no further comparisons arerequired (block 117), whereupon the routine returns.

FIGS. 10A and 10B are a flow diagram showing the routine for comparingpeer collected measures sets 107 for use in the routine of FIG. 8. Peermeasures originate from patient care records for different patients,including groups of disease specific patients or the patient populationin general. The purpose of this routine is to compare peer derivedmeasures to peer derived measures (blocks 122-125), peer derivedmeasures to peer collected measures (blocks 126-129), peer collectedmeasures to peer derived measures (block 131-134), or peer collectedmeasures to peer collected measures (blocks 135-137). Thus, if the firstmeasure being compared is a derived measure (block 120) and the secondmeasure being compared is also a derived measure (block 121), measuresare selected from each collected measures set (block 122). First andsecond derived measures are derived from the selected measures (block123) using the derivation module 63 (shown in FIG. 4). The first andsecond derived measures are then compared (block 124) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting,determining, and comparing (blocks 122-124) are repeated until nofurther comparisons are required (block 115), whereupon the routinereturns.

If the first measure being compared is a derived measure (block 120) butthe second measure being compared is a collected measure (block 121), afirst measure is selected from the first collected measures set (block126). A first derived measure is derived from the first selected measure(block 127) using the derivation module 63 (shown in FIG. 4). The firstderived and second collected measures are then compared (block 128)using the comparison module 62 (also shown in FIG. 4). The steps ofselecting, determining, and comparing (blocks 126-128) are repeateduntil no further comparisons are required (block 129), whereupon theroutine returns.

If the first measure being compared is a collected measure (block 120)but the second measure being compared is a derived measure (block 130),a second measure is selected from the second collected measures set(block 131). A second derived measure is derived from the secondselected measure (block 132) using the derivation module 63 (shown inFIG. 4). The first collected and second derived measures are thencompared (block 133) using the comparison module 62 (also shown in FIG.4). The steps of selecting, determining, and comparing (blocks 131-133)are repeated until no further comparisons are required (block 134),whereupon the routine returns.

If the first measure being compared is a collected measure (block 120)and the second measure being compared is also a collected measure (block130), measures are selected from each collected measures set (block135). The first and second collected measures are then compared (block136) using the comparison module 62 (also shown in FIG. 4). The steps ofselecting and comparing (blocks 135-136) are repeated until no furthercomparisons are required (block 137), whereupon the routine returns.

FIG. 11 is a flow diagram showing the routine for providing feedback 97for use in the method of FIG. 7. The purpose of this routine is toprovide tiered feedback based on the patient status indicator. Fourlevels of feedback are provided with increasing levels of patientinvolvement and medical care intervention. At a first level (block 150),an interpretation of the patient status indicator 54, preferably phrasedin lay terminology, and related health care information is sent to theindividual patient (block 151) using the feedback module 55 (shown inFIG. 3). At a second level (block 152), a notification of potentialmedical concern, based on the analysis and heuristic trends analysis, issent to the individual patient (block 153) using the feedback module 55.At a third level (block 154), the notification of potential medicalconcern is forwarded to the physician responsible for the individualpatient or similar health care professionals (block 155) using thefeedback module 55. Finally, at a fourth level (block 156),reprogramming instructions are sent to the implantable medical device 12(block 157) using the feedback module 55.

FIG. 12 is a block diagram showing a system 200 for providing normalizedvoice feedback from an individual patient 11 in an automated collectionand analysis patient care system, such as the system 10 of FIG. 1. Theremote client 18 includes a microphone 201 and a speaker 202 which isinterfaced internally within the remote client 18 to sound recordationand reproduction hardware. The patient 11 provides spoken feedback intothe microphone 201 in response to voice prompts reproduced by the remoteclient 18 on the speaker 202, as further described below with referenceto FIG. 13. The raw spoken feedback is processed into a normalized setof quality of life measures which each relate to uniform self-assessmentindicators, as further described below with reference to FIG. 15.Alternatively, in a further embodiment of the system 200, the patient 11can provide spoken feedback via a telephone network 203 using a standardtelephone 203, including a conventional wired telephone or a wirelesstelephone, such as a cellular telephone, as further described below withreference to FIG. 20. In the described embodiment, the microphone 201and the speaker 202 are standard, off-the-shelf components commonlyincluded with consumer personal computer systems, as is known in theart.

The system 200 continuously monitors and collects sets of devicemeasures from the implantable medical device 12. To augment the on-goingmonitoring process with a patient's self-assessment of physical andemotional well-being, a quality of life measures set can be recorded bythe remote client 18. Importantly, each quality of life measures set isrecorded substantially contemporaneous to the collection of anidentified collected device measures set. The date and time of day atwhich the quality of life measures set was recorded can be used tocorrelate the quality of life measures set to the collected devicemeasures set recorded closest in time to the quality of life measuresset. The pairing of the quality of life measures set and an identifiedcollected device measures set provides medical practitioners with a morecomplete picture of the patient's medical status by combiningphysiological “hard” machine-recorded data with semi-quantitative “soft”patient-provided data.

FIG. 13 is a block diagram showing the software modules of the remoteclient 18 of the system 200 of FIG. 12. As with the software modules ofthe system 10 of FIG. 1, each module here is also a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU as object or byte code, as is known in the arts. There are twobasic software modules, which functionally define the primary operationsperformed by the remote client 18 in providing normalized voicefeedback: audio prompter 210 and speech engine 214. The remote client 18includes a secondary storage 219, such as a hard drive, a CD ROM player,and the like, within which is stored data used by the software modules.Conceptually, the voice reproduction and recognition functions performedby the audio prompter 210 and speech engine 214 can be describedseparately, but those same functions could also be performed by a singlevoice processing module, as is known in the art.

The audio prompter 210 generates voice prompts 226 which are played backto the patient 11 on the speaker 202. Each voice prompt is in the formof a question or phrase seeking to develop a self-assessment of thepatient's physical and emotional well being. For example, the patient 11might be prompted with, “Are you short of breath?” The voice prompts 226are either from a written script 220 reproduced by speech synthesizer211 or pre-recorded speech 221 played back by playback module 212. Thewritten script 220 is stored within the secondary storage 219 andconsists of written quality of life measure requests. Similarly, thepre-recorded speech 221 is also stored within the secondary storage 219and consists of sound “bites” of recorded quality of life measurerequests in either analog or digital format.

The speech engine 214 receives voice responses 227 spoken by the patient11 into the microphone 201. The voice responses 227 can be unstructured,natural language phrases and sentences. A voice grammar 222 provides alexical structuring for use in determining the meaning of each spokenvoice response 227. The voice grammar 222 allows the speech engine 214to “normalize” the voice responses 227 into recognized quality of lifemeasures 228. Individual spoken words in each voice response 227 arerecognized by a speech recognition module 215 and translated intowritten words. In turn, the written words are parsed into tokens by aparser 216. A lexical analyzer 217 analyzes the tokens as completephrases in accordance with a voice grammar 222 stored within thesecondary storage 219. Finally, if necessary, the individual words arenormalized to uniform terms by a lookup module 218 which retrievessynonyms maintained as a vocabulary 223 stored within the secondarystorage 218. For example, in response to the query, “Are you short ofbreath?,” a patient might reply, “I can hardly breath,” “I am panting,”or “I am breathless.” The speech recognition module 215 would interpretthese phrases to imply dyspnea with a corresponding quality of lifemeasure indicating an awareness by the patient of abnormal breathing. Inthe described embodiment, the voice reproduction and recognitionfunctions can be performed by the various natural voice softwareprograms licensed by Dragon Systems, Inc., Newton, Mass. Alternatively,the written script 220, voice grammar 222, and vocabulary 223 could beexpressed as a script written in a voice page markup language forinterpretation by a voice browser operating on the remote client 18. Twoexemplary voice page description languages include the VoxML markuplanguage, licensed by Motorola, Inc., Chicago, Ill., and described athttp://www.voxml.com, and the Voice extensible Markup Language (VXML),currently being jointly developed by AT&T, Motorola, LucentTechnologies, and IBM, and described at http://www.vxmlforum.com. Themodule functions are further described below in more detail beginningwith reference to FIGS. 16A-16B.

FIG. 14 is a block diagram showing the software modules of the serversystem 16 of the system 200 of FIG. 12. The database module 51,previously described above with reference to FIG. 3, also receives thecollected quality of life measures set 228 from the remote client 18,which the database module 51 stores into the appropriate patient carerecord in the database 52. The date and time of day 236 (shown in FIG.15) of the quality of life measures set 228 is matched to the date andtime of day 73 (shown in FIG. 5) of the collected measures set 50recorded closest in time to the quality of life measures set 228. Thematching collected measures set 50 is identified in the patient carerecord and can be analyzed with the quality of life measures set 228 bythe analysis module 53, such as described above with reference to FIG.8.

FIG. 15 is a database schema showing, by way of example, theorganization of a quality of life record 230 for cardiac patient carestored as part of a patient care record in the database 17 of the system200 of FIG. 12. A quality of life score is a semi-quantitativeself-assessment of an individual patient's physical and emotional wellbeing. Non-commercial, non-proprietary standardized automated quality oflife scoring systems are readily available, such as provided by the DukeActivities Status Indicator. For example, for a cardiac patient, thequality of life record 230 stores the following information: healthwellness 231, shortness of breath 232, energy level 233, chestdiscomfort 235, time of day 234, and other quality of life measures aswould be known to one skilled in the art. Other types of quality of lifemeasures are possible.

A quality of life indicator is a vehicle through which a patient canremotely communicate to the patient care system how he or she issubjectively feeling. The quality of life indicators can includesymptoms of disease. When tied to machine-recorded physiologicalmeasures, a quality of life indicator can provide valuable additionalinformation to medical practitioners and the automated collection andanalysis patient care system 200 not otherwise discernible withouthaving the patient physically present. For instance, a scoring systemusing a scale of 1.0 to 10.0 could be used with 10.0 indicating normalwellness and 1.0 indicating severe health problems. Upon the completionof an initial observation period, a patient might indicate a healthwellness score 231 of 5.0 and a cardiac output score of 5.0. After onemonth of remote patient care, the patient might then indicate a healthwellness score 231 of 4.0 and a cardiac output score of 4.0 and a weeklater indicate a health wellness score 231 of 3.5 and a cardiac outputscore of 3.5. Based on a comparison of the health wellness scores 231and the cardiac output scores, the system 200 would identify a trendindicating the necessity of potential medical intervention while acomparison of the cardiac output scores alone might not lead to the sameprognosis.

FIGS. 16A-16B are flow diagrams showing a method 239 for providingnormalized voice feedback from an individual patient 11 in an automatedcollection and analysis patient care system 200. As with the method 90of FIG. 7, this method is also implemented as a conventional computerprogram and performs the same set of steps as described with referenceto FIG. 7 with the following additional functionality. First, voicefeedback spoken by the patient 11 into the remote client 18 is processedinto a quality of life measures set 228 (block 240), as furtherdescribed below with reference to FIG. 17. The voice feedback is spokensubstantially contemporaneous to the collection of an identified devicemeasures set 50. The appropriate collected device measures set 50 can bematched to and identified with (not shown) the quality of life measuresset 228 either by matching their respective dates and times of day or bysimilar means, either by the remote client 18 or the server system 16.The quality of life measures set 228 and the identified collectedmeasures set 50 are sent over the internetwork 15 to the server system16 (block 241). Note the quality of life measures set 228 and theidentified collected measures set 50 both need not be sent over theinternetwork 15 at the same time, so long as the two sets are ultimatelypaired based on, for example, date and time of day. The quality of lifemeasures set 228 and the identified collected measures set 50 arereceived by the server system 16 (block 242) and stored in theappropriate patient care record in the database 52 (block 243). Finally,the quality of life measures set 228, identified collected measures set50, and one or more collected measures sets 50 are analyzed (block 244)and feedback, including a patient status indicator 54 (shown in FIG.14), is provided to the patient (block 245).

FIG. 17 is a flow diagram showing the routine for processing voicefeedback 240 for use in the method of FIGS. 16A-16B. The purpose of thisroutine is to facilitate a voice interactive session with the patient 11during which is developed a normalized set of quality of life measures.Thus, the remote client 18 requests a quality of life measure via avoice prompt (block 250), played on the speaker 202 (shown in FIG. 13),as further described below with reference to FIG. 18. The remote client18 receives the spoken feedback from the patient 11 (block 251) via themicrophone 201 (shown in FIG. 13). The remote client 18 recognizesindividual words in the spoken feedback and translates those words intowritten words (block 252), as further described below with reference toFIG. 19. The routine returns at the end of the voice interactivesession.

FIG. 18 is a flow diagram showing the routine for requesting a qualityof life measure 251 for use in the routine 240 of FIG. 17. The purposeof this routine is to present a voice prompt 226 to the user via thespeaker 202. Either pre-recorded speech 221 or speech synthesized from awritten script 220 can be used. Thus, if synthesized speech is employedby the remote client 18 (block 260), a written script, such as a voicemarkup language script, specifying questions and phrases which with torequest quality of life measures is stored (block 261) on the secondarystorage 219 of the remote client 18. Each written quality of lifemeasure request is retrieved by the remote client 18 (block 262) andsynthesized into speech for playback to the patient 11 (block 263).Alternatively, if pre-recorded speech is employed by the remote client18 (block 260), pre-recorded voice “bites” are stored (block 264) on thesecondary storage 219 of the remote client 18. Each pre-recorded qualityof life measure request is retrieved by the remote client 18 (block 265)and played back to the patient 11 (block 266). The routine then returns.

FIG. 19 is a flow diagram showing the routine for recognizing andtranslating individual spoken words 252 for use in the routine 240 ofFIG. 17. The purpose of this routine is to receive and interpret afree-form voice response 227 from the user via the microphone 201.First, a voice grammar consisting of a lexical structuring of words,phrases, and sentences is stored (block 270) on the secondary storage219 of the remote client 18. Similarly, a vocabulary of individual wordsand their commonly accepted synonyms is stored (block 271) on thesecondary storage 219 of the remote client 18. After individual words inthe voice feedback are recognized (block 272), the individual words areparsed into tokens (block 273). The voice feedback is then lexicallyanalyzed using the tokens and in accordance with the voice grammar 222(block 274) to determine the meaning of the voice feedback. Ifnecessary, the vocabulary 223 is referenced to lookup synonyms of theindividual words (block 275). The routine then returns.

FIG. 20 is a block diagram showing the software modules of the serversystem in a further embodiment of the system 200 of FIG. 12. Thefunctionality of the remote client 18 in providing normalized voicefeedback is incorporated directly into the server system 16. The system200 of FIG. 12 requires the patient 11 to provide spoken feedback via alocally situated remote client 18. However, the system 280 enables apatient 11 to alternatively provide spoken feedback via a telephonenetwork 203 using a standard telephone 203, including a conventionalwired telephone or a wireless telephone, such as a cellular telephone.The server system 16 is augmented to include the audio prompter 210, thespeech engine 214, and the data stored in the secondary storage 219. Atelephonic interface 280 interfaces the server system 16 to thetelephone network 203 and receives voice responses 227 and sends voiceprompts 226 to and from the server system 16. Telephonic interfacingdevices are commonly known in the art.

Therefore, through the use of the collected measures sets, the presentinvention makes possible immediate access to expert medical care at anytime and in any place. For example, after establishing and registeringfor each patient an appropriate baseline set of measures, the databaseserver could contain a virtually up-to-date patient history, which isavailable to medical providers for the remote diagnosis and preventionof serious illness regardless of the relative location of the patient ortime of day.

Moreover, the gathering and storage of multiple sets of critical patientinformation obtained on a routine basis makes possible treatmentmethodologies based on an algorithmic analysis of the collected datasets. Each successive introduction of a new collected measures set intothe database server would help to continually improve the accuracy andeffectiveness of the algorithms used. In addition, the present inventionpotentially enables the detection, prevention, and cure of previouslyunknown forms of disorders based on a trends analysis and by across-referencing approach to create continuously improving peer-groupreference databases.

Similarly, the present invention makes possible the provision of tieredpatient feedback based on the automated analysis of the collectedmeasures sets. This type of feedback system is suitable for use in, forexample, a subscription based health care service. At a basic level,informational feedback can be provided by way of a simple interpretationof the collected data. The feedback could be built up to provide agradated response to the patient, for example, to notify the patientthat he or she is trending into a potential trouble zone. Humaninteraction could be introduced, both by remotely situated and localmedical practitioners. Finally, the feedback could include directinterventive measures, such as remotely reprogramming a patient's IPG.

Finally, the present invention allows “live” patient voice feedback tobe captured simultaneously with the collection of physiological measuresby their implantable medical device. The voice feedback is normalized toa standardized set of quality of life measures which can be analyzed ina remote, automated fashion. The voice feedback could also be coupledwith visual feedback, such as through digital photography or video, toprovide a more complete picture of the patient's physical well-being.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A system for providing patient status feedback via an automated patient care system with speech-based wellness monitoring, comprising: an implantable medical device collecting device measures on a substantially continuous basis from an implant recipient; a database module receiving the device measures as physiological measures for storage into a patient care record, the physiological measures comprising at least one of collected or derived physiological measures; a remote client obtaining patient wellness indicators through voice feedback provided by the implant recipient substantially contemporaneous to the collection of at least one set of the device measures; a feedback module processing the voice feedback against a stored speech vocabulary into normalized quality of life measures for storage into the patient care record; and an analysis module analyzing the physiological measures and the quality of life measures stored in the patient care record relative to at least one of other physiological measures and other quality of life measures to generate patient status feedback.
 2. A system according to claim 1, further comprising: the analysis module comparing the physiological measures and quality of life measures stored in the patient care record to at least one of either collected or derived physiological measures and quality of life measures stored in patient care records for the individual patient, a patient peer group, and a patient population.
 3. A system according to claim 1, further comprising: the feedback module providing progressive feedback, comprising at least one of an interpretation of the patient status, a notification of potential medical concern based on the patient status sent to at least one of the implant recipient and medical personnel, and a set of reprogramming instructions based on the patient status sent to the implantable medical device.
 4. A system according to claim 1, further comprising: the feedback module requesting the voice feedback through pre-determined prompts corresponding to the quality of life measures and parsing the voice feedback in accordance with a voice grammar to normalize the voice feedback.
 5. A method for providing patient status feedback via an automated patient care system with speech-based wellness monitoring, comprising: collecting device measures through an implantable medical device on a substantially continuous basis from an implant recipient; receiving the device measures as physiological measures for storage into a patient care record, the physiological measures comprising at least one of collected or derived physiological measures; obtaining patient wellness indicators through voice feedback provided by the implant recipient substantially contemporaneous to the collection of at least one set of the device measures; processing the voice feedback against a stored speech vocabulary into normalized quality of life measures for storage into the patient care record; and analyzing the physiological measures and the quality of life measures stored in the patient care record relative to at least one of other physiological measures and other quality of life measures to generate patient status feedback.
 6. A method according to claim 5, further comprising: comparing the physiological measures and quality of life measures stored in the patient care record to at least one of either collected or derived physiological measures and quality of life measures stored in patient care records for the individual patient, a patient peer group, and a patient population.
 7. A method according to claim 5, further comprising: providing progressive feedback, comprising at least one of an interpretation of the patient status, a notification of potential medical concern based on the patient status sent to at least one of the implant recipient and medical personnel, and a set of reprogramming instructions based on the patient status sent to the implantable medical device.
 8. A method according to claim 5, further comprising: requesting the voice feedback through pre-determined prompts corresponding to the quality of life measures; and parsing the voice feedback in accordance with a voice grammar to normalize the voice feedback.
 9. A computer-readable storage medium holding code for providing patient status feedback via an automated patient care system with speech-based wellness monitoring, comprising: collecting device measures through an implantable medical device on a substantially continuous basis from an implant recipient; receiving the device measures as physiological measures for storage into a patient care record, the physiological measures comprising at least one of collected or derived physiological measures; obtaining patient wellness indicators through voice feedback provided by the implant recipient substantially contemporaneous to the collection of at least one set of the device measures; processing the voice feedback against a stored speech vocabulary into normalized quality of life measures for storage into the patient care record; and analyzing the physiological measures and the quality of life measures stored in the patient care record relative to at least one of other physiological measures and other quality of life measures to generate patient status feedback.
 10. A storage medium according to claim 9, further comprising: comparing the physiological measures and quality of life measures stored in the patient care record to at least one of either collected or derived physiological measures and quality of life measures stored in patient care records for the individual patient, a patient peer group, and a patient population.
 11. A storage medium according to claim 9, further comprising: providing progressive feedback, comprising at least one of an interpretation of the patient status, a notification of potential medical concern based on the patient status sent to at least one of the implant recipient and medical personnel, and a set of reprogramming instructions based on the patient status sent to the implantable medical device.
 12. A storage medium according to claim 9, further comprising: requesting the voice feedback through pre-determined prompts corresponding to the quality of life measures; and parsing the voice feedback in accordance with a voice grammar to normalize the voice feedback.
 13. A system for interactively monitoring patient status in an automated patient care system using voice feedback, comprising: a physiological measures monitoring subsystem, comprising: an implantable medical device collecting device measures on a substantially continuous basis from an implant recipient; a database module periodically storing the device measures as at least one of collected or derived physiological measures into an individual patient care record; a quality of life measures monitoring subsystem, comprising: a remote client obtaining patient wellness indicators through voice feedback provided by the implant recipient substantially contemporaneous to the collection of the device measures; a feedback module processing the voice feedback against a stored speech grammar and vocabulary; a database module storing the processed voice feedback as standardized quality of life measures into the patient care record; and an analysis module recurrently evaluating the physiological measures and the quality of life measures from the patient care record against at least one of other physiological measures and other quality of life measures to generate a patient status indicator.
 14. A method for interactively monitoring patient status in an automated patient care system using voice feedback, comprising: monitoring physiological measures for an implant recipient, comprising: collecting device measures through an implantable medical device on a substantially continuous basis from the implant recipient; periodically storing the device measures as at least one of collected or derived physiological measures into an individual patient care record; monitoring quality of life measures for the implant recipient, comprising: obtaining patient wellness indicators through voice feedback provided by the implant recipient substantially contemporaneous to the collection of the device measures; processing the voice feedback against a stored speech grammar and vocabulary; storing the processed voice feedback as standardized quality of life measures into the patient care record; and recurrently evaluating the physiological measures and the quality of life measures from the patient care record against at least one of other physiological measures and other quality of life measures to generate a patient status indicator.
 15. A computer-readable storage medium holding code for interactively monitoring patient status in an automated patient care system using voice feedback, comprising: monitoring physiological measures for an implant recipient, comprising: collecting device measures through an implantable medical device on a substantially continuous basis from the implant recipient; periodically storing the device measures as at least one of collected or derived physiological measures into an individual patient care record; monitoring quality of life measures for the implant recipient, comprising: obtaining patient wellness indicators through voice feedback provided by the implant recipient substantially contemporaneous to the collection of the device measures; processing the voice feedback against a stored speech grammar and vocabulary; storing the processed voice feedback as standardized quality of life measures into the patient care record; and recurrently evaluating the physiological measures and the quality of life measures from the patient care record against at least one of other physiological measures and other quality of life measures to generate a patient status indicator. 